{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/challenges-of-real-world-reinforcement","title":"Challenges of Real-World Reinforcement Learning","arxiv_id":"1904.12901","date":"2019-04-29","proceeding":null,"authors":["Gabriel Dulac-Arnold","Daniel Mankowitz","Todd Hester"],"abstract":"Reinforcement learning (RL) has proven its worth in a series of artificial\ndomains, and is beginning to show some successes in real-world scenarios.\nHowever, much of the research advances in RL are often hard to leverage in\nreal-world systems due to a series of assumptions that are rarely satisfied in\npractice. We present a set of nine unique challenges that must be addressed to\nproductionize RL to real world problems. For each of these challenges, we\nspecify the exact meaning of the challenge, present some approaches from the\nliterature, and specify some metrics for evaluating that challenge. An approach\nthat addresses all nine challenges would be applicable to a large number of\nreal world problems. We also present an example domain that has been modified\nto present these challenges as a testbed for practical RL research.","url_abs":"http://arxiv.org/abs/1904.12901v1","url_pdf":"http://arxiv.org/pdf/1904.12901v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"challenges-of-real-world-reinforcement","repo_url":"https://github.com/google-research/realworldrl_suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.12901","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}